In early researches, many kinds of detection frameworks are proposed in crowd counting. The pivotal features from human body are extracted by the well-trained classifiers such as HOG [1]}, Random Forest [2]}, and Haar wavelets [3]}, then the classifier outputs the positive samples. The total number of positive samples represents the crowd count. [4]}, [5]}, [6]} complete the crowd counting task by using the aforementioned method. And [7]} leverages adaptive thresholds to binarize the image to detect the crowd. They can get a good result in the sparse crowd scene. However, when the crowd density becomes high, some persons are too small to be detected.
